OBJECTIVE: Psychopathology research has relied on discrete diagnoses, which neglects the unique manifestations of each individual's pathology. Borderline personality disorder combines interpersonal, affective, and behavioral regulation impairments making it particularly ill-suited to a "one size fits all" diagnosis. Clinical assessment and case formulation involve understanding and developing a personalized model for each patient's contextualized dynamic processes, and research would benefit from a similar focus on the individual. METHOD: We use group iterative multiple model estimation, which estimates a model for each individual and identifies general or shared features across individuals, in both a mixed-diagnosis sample (N = 78) and a subsample with a single diagnosis (n = 24). RESULTS: We found that individuals vary widely in their dynamic processes in affective and interpersonal domains both within and across diagnoses. However, there was some evidence that dynamic patterns relate to transdiagnostic baseline measures. We conclude with descriptions of 2 person-specific models as an example of the heterogeneity of dynamic processes. CONCLUSIONS: The idiographic models presented here join a growing literature showing that the individuals differ dramatically in the total patterning of these processes, even as key processes are shared across individuals. We argue that these processes are best estimated in the context of person-specific models, and that so doing may advance our understanding of the contextualized dynamic processes that could identify maintenance mechanisms and treatment targets. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
OBJECTIVE: Psychopathology research has relied on discrete diagnoses, which neglects the unique manifestations of each individual's pathology. Borderline personality disorder combines interpersonal, affective, and behavioral regulation impairments making it particularly ill-suited to a "one size fits all" diagnosis. Clinical assessment and case formulation involve understanding and developing a personalized model for each patient's contextualized dynamic processes, and research would benefit from a similar focus on the individual. METHOD: We use group iterative multiple model estimation, which estimates a model for each individual and identifies general or shared features across individuals, in both a mixed-diagnosis sample (N = 78) and a subsample with a single diagnosis (n = 24). RESULTS: We found that individuals vary widely in their dynamic processes in affective and interpersonal domains both within and across diagnoses. However, there was some evidence that dynamic patterns relate to transdiagnostic baseline measures. We conclude with descriptions of 2 person-specific models as an example of the heterogeneity of dynamic processes. CONCLUSIONS: The idiographic models presented here join a growing literature showing that the individuals differ dramatically in the total patterning of these processes, even as key processes are shared across individuals. We argue that these processes are best estimated in the context of person-specific models, and that so doing may advance our understanding of the contextualized dynamic processes that could identify maintenance mechanisms and treatment targets. (PsycINFO Database Record (c) 2020 APA, all rights reserved).